Lung Ultrasound as a Predictor of Mechanical Ventilation in Neonates Older Than 32 Weeks

NCT ID: NCT02449863

Last Updated: 2015-05-20

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 105 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2014-01-31
• Study Completion Date: 2014-12-31

Brief Summary

Neonatal respiratory distress prognosis may be difficult to estimate at admission. Lung ultrasound is a useful diagnostic tool that is quick, requires little training and is radiation free. This study analyzes whether early lung ultrasound can predict respiratory failure.

Detailed Description

Neonatal respiratory distress prognosis may be difficult to estimate at admission. Lung ultrasound is a useful diagnostic tool that is quick, requires little training and is radiation free. This study analyzes whether early lung ultrasound can predict respiratory failure.

Methods This study was conducted from January to December 2014 at Hospital Sant Joan de Déu (Esplugues de Llobregat, Barcelona, Spain), a third-level hospital with 3300 births per year and a neonatal intensive care unit with annual admission of 700 patients.

Local institutional review board of Hospital Sant Joan de Déu approved the protocol (project approval number PIC-07-15) and written informed consent was obtained from all parents.

Patients older than 32 weeks admitted to the neonatal intensive care unit with respiratory distress who were not on invasive mechanical ventilation (MV) were eligible for recruitment.

A single operator, a neonatologist skilled in lung and heart sonography, performed the examinations. Images were then analysed by another neonatologist with less experience in LUS. He was blind to the perinatal history and chest radiography of the newborns and unaware of the clinical diagnosis. Infants were from a non-consecutive convenience sample recruited when the operator was available for the execution of LUS in the first 2 hours of life.

Examinations were performed with a portable device (Siemens Acuson X) using a 10MHz linear probe and previously warmed gel. Eight video clips were stored at each examination, which was performed at the patient's bedside, with the neonate placed in a supine position. In each hemithorax 4 regions were evaluated: parasternal area, anterolateral axillary area, posterior axillary area, and the fifth intercostal space, by means of a transversal scan. The LUS procedures were carried out in 1.5-2 minutes.

Infants were classified into 2 groups, according to the LUS pattern:

• Low risk: Normal, transient tachypnea of the newborn.
• High risk: Respiratory distress syndrome, meconium aspiration syndrome, pneumothorax, pneumonia.

A second investigator made the same classification after reading chest x-ray pictures. Respiratory failure was defined as the need for invasive mechanical ventilation during the first day of life.

A single consultant, a neonatologist expert in lung disease, also blinded to the patient's perinatal history and clinical condition, made the x-ray diagnosis.

Finally, another consultant neonatologist made the final clinical diagnosis taking into account complete patient's medical history except LUS information.

Perinatal and anthropometric data (gestational age, weight, sex, antenatal steroids, and delivery method) were collected from clinical charts and data regarding neonatal respiratory evolution (hours of oxygen and ventilation, respiratory support-NIV, conventional MV, high frequency oscillatory ventilation or extracorporeal membrane oxygenation-and need for surfactant) were collected during admission.

Statistics All data were analysed using IBM SPSS version 20.0 (IBM Corporation, USA). Clinical features and respiratory outcomes were summarized using descriptive statistics (frequency distribution for categorical data and mean and standard deviation or median and interquartile range for continuous data). Univariate analysis included the Chi-square test and Fisher's exact test, as appropriate, for categorical comparisons, and t-Student or Mann-Whitney test for continuous variables. Wilson method was used to compute confidence interval (CI). Cohen´s kappa coefficient was provided to assess agreement between sonographic and radiologic risk patterns. Predictive values and related parameters (sensibility, specificity and likelihood ratios) were calculated for both diagnostic tests (sonographic pattern risk and radiologic pattern risk); ROC analysis was used to assess efficiency. CI of Area Under the Curve was obtained by the exact method (Clopper-Pearson). All hypothesis tests were two sided and p value less than 0.05 were considered statistically significant.

Conditions

Ultrasonic Diagnosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Low risk ultrasound

Patients with a low risk ultrasound

Lung ultrasound

Intervention Type: OTHER

Lung ultrasound performed to newborns with respiratory distress

High risk ultrasound

Patients with a high risk ultrasound

Lung ultrasound

Intervention Type: OTHER

Lung ultrasound performed to newborns with respiratory distress

Interventions

Lung ultrasound

Lung ultrasound performed to newborns with respiratory distress

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Patients older than 32 weeks admitted to the neonatal intensive care unit with respiratory distress who were not on invasive mechanical ventilation (MV) were eligible for recruitment.

Exclusion Criteria

• Patients younger than 32 weeks
• Patients with mechanic ventilation ar admission

• Minimum Eligible Age: 32 Weeks
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Hospital Sant Joan de Deu

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Javier Rodríguez-Fanjul, M.D.

Role: PRINCIPAL_INVESTIGATOR

Hospital Sant Joan de Déu. Esplugues de Llobregat. Spain

Locations

Hospital Sant Joan de Déu

Esplugues de Llobregat, , Spain

Javier Rodriguez Fanjul

Esplugues de Llobregat, , Spain

Countries

Spain

Other Identifiers

PIC-07-15

Identifier Type: -

Identifier Source: org_study_id